karlacruzmartinez92@gmail.com

rm(list = ls())

library(htmltools)
library(htmlwidgets)
library(AER)
library(plm)
library(CGPfunctions)
library(foreign)
library(gplots)
library(haven)
library(dplyr)        # wickham2020b
library(DT)           # xie2020
library(ggplot2)      # wickham2016
library(Hmisc)        # harrell2020
library(kableExtra)   # zhu2021
library(knitr)        # xie2014
library(RColorBrewer) # neuwirth2014
library(reshape2)     # wickham2007
library(scales)  
library(readxl)
library(GGally)
library(Hmisc)
library(corrplot)
library(PerformanceAnalytics)# wickham2020

INTRODUCCIÓN

Actualmente México vive una ola de violencia. De acuerdo con algunos especialista, esto tiene relación con la presencia de grupos criminales y las políticas pública ejercida contra los mismos. Bajo este panorama, se pretende responder a ¿cómo es el comportamiento del crimen en México? ¿Los homicidios tienen un comportamiento generalizado en todo el país? ¿Cuáles son las áreas geográficas con mayores tasas de homicidios por armas de fuego? ¿Cómo han variado las tasas de homicidios a lo largo del tiempo entre hombres y mujeres? ¿Existe una relación entre el trasiego de drogas y los homicidios a nivel entidad federativa?

ESTABLECER EL CONTEXTO

Las actividades del crimen organizado tienen un impacto significativo en las comunidades y en los países, lo que ha llevado a diversos estados a combatirlo. Una de estas actividades es el mercado de drogas ilegales, el cual se encuentra en manos de organizaciones criminales desde su surgimiento a mitad del siglo XIX, ya que hay en juego prolíferos dividendos. En 2017, Global Financial Integrity estimó las ganancias totales anuales de once mercados delcrimen organizado, siendo el mercado de sustancias psicoactivas el segundo más lucrativo. Las ganancias calculadas oscilaron entre 426 mil millones y 652 mil millones de dólares anuales. Algunas de las razones por las cuales se origina dicho mercado de drogas ilegales, como coloquialmente se conoce, es la alta demanda de los países desarrollados y las limitadas condiciones socioeconómicas en los países productores, en donde hay un dominio de las organizaciones criminales. En 2017, el consumo de drogas aumentó 30 % con respecto al 2009. En especial incrementó el consumo de opioides, sustancias que derivan del jugo de la amapola o adormidera Papaver somniferum. El consumo de estas sustancias creció 56 % respecto al 2016; la heroína y el opio fueron las drogas más usadas. Aunado a esto, en las últimas dos décadas, la producción a gran escala de cultivos de amapola ha estado en ascenso en los principales países productores como son: Afganistán, Myanmar y México4 según United Nations Office on Drugs and Crime (UNODC) (2019). En el caso de México, entre 2015 y 2017, se registró una expansión del cultivo de amapola del 21 % en diversas zonas del país, de acuerdo con las cifras que presentó UNODC en 2018. Esta expansión podría estár relacionada con la alta demanda de opioides5 por Estados Unidos. Evans W., Lieber, E. y Power, P. (2019), documentan que, en 2010, Estados Unidos reportó un incremento de muertes por sobredosis de heroína como consecuencia de la reformulación de OxyContin , la cual buscaba evitar el abuso de opioides recetados. Por un lado, esta medida bien intencionada condujo a una serie de efectos adversos como fue el abuso de drogas ilegales,como la heroína. Y por el otro, un incremento en la violencia por la couta del mercado de la heroína entre las organizaciones criminales, como establece Sobrino, F. (2019), en México.

DATOS

Se construyó un panel de datos a nivel municipal, que para efectos de este trabajo se realizó el análisis exploratorio a nivel entidad federativa. Los datos son públicos: -Defunciones, los cuales fueron extraídos de lo Registros Administrativos del Instituto Nacional de Estadística y Geografía. -Incautaciones, son parte del México Unido contra la Delincuencia. -Precios internacionales de la droga, son de UNODC.

rm(list = ls())

## Directorio de trabajo

setwd("/Users/karlacruz/Desktop/final_project/data_bases/result")

base <-read_dta("finaldataENT.dta")
#modelo <-read_dta("finaldataMUN.dta")


head(base)
## # A tibble: 6 × 97
##   anio_ocur cve_ent ent   nom_ent  homicide homicide_man homicide_woman homiguns
##       <dbl>   <dbl> <chr> <chr>       <dbl>        <dbl>          <dbl>    <dbl>
## 1      1990      22 22    Queréta…        0            0              0        0
## 2      1990       2 2     Baja Ca…        2            2              0        1
## 3      1990       9 9     Ciudad …        0            0              0        0
## 4      1990       8 8     Chihuah…        3            3              0        2
## 5      1990      21 21    Puebla          0            0              0        0
## 6      1990       4 4     Campeche        0            0              0        0
## # ℹ 89 more variables: homigunsman <dbl>, homigunswoman <dbl>,
## #   AmpFen_SEDENA <dbl>, AseFen_SEDENA <dbl>, AseCoc_SEDENA <dbl>,
## #   AseGomOpio_SEDENA <dbl>, AseHer_SEDENA <dbl>, AseMar_SEDENA <dbl>,
## #   AseMet_SEDENA <dbl>, HecAma_fum_SEDENA <dbl>, HecAma_man_SEDENA <dbl>,
## #   HecMar_fum_SEDENA <dbl>, HecMar_man_SEDENA <dbl>, IncCoc_SEDENA <dbl>,
## #   IncGomOpio_SEDENA <dbl>, IncHer_SEDENA <dbl>, IncMar_SEDENA <dbl>,
## #   IncMet_SEDENA <dbl>, IncSemAma_SEDENA <dbl>, IncSemMar_SEDENA <dbl>, …
summary(base)
##    anio_ocur       cve_ent          ent              nom_ent         
##  Min.   :1990   Min.   : 1.00   Length:1056        Length:1056       
##  1st Qu.:1998   1st Qu.: 8.75   Class :character   Class :character  
##  Median :2006   Median :16.50   Mode  :character   Mode  :character  
##  Mean   :2006   Mean   :16.50                                        
##  3rd Qu.:2014   3rd Qu.:24.25                                        
##  Max.   :2022   Max.   :32.00                                        
##                                                                      
##     homicide        homicide_man    homicide_woman       homiguns     
##  Min.   :   0.00   Min.   :   0.0   Min.   :   0.00   Min.   :   0.0  
##  1st Qu.:  26.75   1st Qu.:  22.0   1st Qu.:   3.00   1st Qu.:   8.0  
##  Median : 176.00   Median : 156.5   Median :  23.00   Median :  93.0  
##  Mean   : 509.13   Mean   : 449.3   Mean   :  56.78   Mean   : 327.6  
##  3rd Qu.: 635.00   3rd Qu.: 568.5   3rd Qu.:  68.00   3rd Qu.: 410.2  
##  Max.   :7886.00   Max.   :6362.0   Max.   :1056.00   Max.   :6004.0  
##                                                                       
##   homigunsman     homigunswoman    AmpFen_SEDENA      AseFen_SEDENA   
##  Min.   :   0.0   Min.   :  0.00   Min.   :  0.0000   Min.   :  0.00  
##  1st Qu.:   7.0   1st Qu.:  0.00   1st Qu.:  0.0000   1st Qu.:  0.00  
##  Median :  86.0   Median :  7.00   Median :  0.0000   Median :  0.00  
##  Mean   : 300.1   Mean   : 26.89   Mean   :  0.6903   Mean   :  1.37  
##  3rd Qu.: 378.5   3rd Qu.: 29.25   3rd Qu.:  0.0000   3rd Qu.:  0.00  
##  Max.   :5188.0   Max.   :804.00   Max.   :521.0000   Max.   :286.05  
##                                                                       
##  AseCoc_SEDENA      AseGomOpio_SEDENA AseHer_SEDENA    AseMar_SEDENA      
##  Min.   :   0.000   Min.   :  0.000   Min.   :  0.00   Min.   :     0.00  
##  1st Qu.:   0.000   1st Qu.:  0.000   1st Qu.:  0.00   1st Qu.:     0.00  
##  Median :   0.000   Median :  0.000   Median :  0.00   Median :    29.98  
##  Mean   :  60.187   Mean   :  8.254   Mean   :  2.97   Mean   :  5339.53  
##  3rd Qu.:   1.055   3rd Qu.:  0.000   3rd Qu.:  0.00   3rd Qu.:  2176.59  
##  Max.   :4711.415   Max.   :995.399   Max.   :280.74   Max.   :275652.79  
##                                                                           
##  AseMet_SEDENA     HecAma_fum_SEDENA HecAma_man_SEDENA  HecMar_fum_SEDENA
##  Min.   :    0.0   Min.   :   0.00   Min.   :   0.000   Min.   :   0.0   
##  1st Qu.:    0.0   1st Qu.:   0.00   1st Qu.:   0.000   1st Qu.:   0.0   
##  Median :    0.0   Median :   0.00   Median :   0.000   Median :   0.0   
##  Mean   :  191.5   Mean   :  55.95   Mean   : 338.199   Mean   :  61.6   
##  3rd Qu.:    0.0   3rd Qu.:   0.00   3rd Qu.:   9.056   3rd Qu.:   0.0   
##  Max.   :29675.9   Max.   :2178.34   Max.   :9494.069   Max.   :3594.7   
##                                                                          
##  HecMar_man_SEDENA  IncCoc_SEDENA    IncGomOpio_SEDENA IncHer_SEDENA     
##  Min.   :   0.000   Min.   : 0.000   Min.   : 0.0000   Min.   :0.000000  
##  1st Qu.:   0.000   1st Qu.: 0.000   1st Qu.: 0.0000   1st Qu.:0.000000  
##  Median :   3.703   Median : 0.000   Median : 0.0000   Median :0.000000  
##  Mean   : 348.035   Mean   : 0.182   Mean   : 0.2108   Mean   :0.003922  
##  3rd Qu.: 161.418   3rd Qu.: 0.000   3rd Qu.: 0.0000   3rd Qu.:0.000000  
##  Max.   :8012.398   Max.   :70.765   Max.   :17.5880   Max.   :2.252000  
##                                                                          
##  IncMar_SEDENA      IncMet_SEDENA       IncSemAma_SEDENA  IncSemMar_SEDENA 
##  Min.   :     0.0   Min.   :0.0000000   Min.   :   0.00   Min.   :   0.00  
##  1st Qu.:     0.0   1st Qu.:0.0000000   1st Qu.:   0.00   1st Qu.:   0.00  
##  Median :    11.5   Median :0.0000000   Median :   0.00   Median :   0.00  
##  Mean   : 14978.3   Mean   :0.0005115   Mean   :  30.89   Mean   : 144.63  
##  3rd Qu.:  3173.8   3rd Qu.:0.0000000   3rd Qu.:   1.00   3rd Qu.:  30.24  
##  Max.   :623229.3   Max.   :0.1100000   Max.   :2118.42   Max.   :6280.11  
##                                                                            
##  LabCoc_SEDENA      LabHer_SEDENA     LabMet_SEDENA     PasFen_SEDENA    
##  Min.   :0.000000   Min.   :0.00000   Min.   :  0.000   Min.   :      0  
##  1st Qu.:0.000000   1st Qu.:0.00000   1st Qu.:  0.000   1st Qu.:      0  
##  Median :0.000000   Median :0.00000   Median :  0.000   Median :      0  
##  Mean   :0.000947   Mean   :0.01989   Mean   :  1.959   Mean   :   7174  
##  3rd Qu.:0.000000   3rd Qu.:0.00000   3rd Qu.:  0.000   3rd Qu.:      0  
##  Max.   :1.000000   Max.   :3.00000   Max.   :350.000   Max.   :2450350  
##                                                                          
##  PlaAma_fum_SEDENA PlaAma_man_SEDENA   PlaMar_fum_SEDENA PlaMar_man_SEDENA
##  Min.   :    0.0   Min.   :     0.00   Min.   :    0.0   Min.   :    0    
##  1st Qu.:    0.0   1st Qu.:     0.00   1st Qu.:    0.0   1st Qu.:    0    
##  Median :    0.0   Median :     0.00   Median :    0.0   Median :   31    
##  Mean   :  380.2   Mean   :  2942.79   Mean   :  490.5   Mean   : 3690    
##  3rd Qu.:    0.0   3rd Qu.:    91.25   3rd Qu.:    0.0   3rd Qu.: 1441    
##  Max.   :19514.0   Max.   :121011.00   Max.   :24691.0   Max.   :91882    
##                                                                           
##  SemAma_SEDENA      SemMar_SEDENA      AseCoc_SEMAR      AseHer_SEMAR     
##  Min.   :   0.000   Min.   :   0.00   Min.   :    0.0   Min.   : 0.00000  
##  1st Qu.:   0.000   1st Qu.:   0.00   1st Qu.:    0.0   1st Qu.: 0.00000  
##  Median :   0.000   Median :   0.00   Median :    0.0   Median : 0.00000  
##  Mean   :   6.953   Mean   :  25.49   Mean   :  136.7   Mean   : 0.05541  
##  3rd Qu.:   0.000   3rd Qu.:   5.00   3rd Qu.:    0.0   3rd Qu.: 0.00000  
##  Max.   :1122.052   Max.   :2111.09   Max.   :23365.0   Max.   :33.80580  
##                                                                           
##   AseMar_SEMAR      AseMet_SEMAR      M2Ama_SEMAR       M2Mar_SEMAR     
##  Min.   :    0.0   Min.   :    0.0   Min.   :      0   Min.   :      0  
##  1st Qu.:    0.0   1st Qu.:    0.0   1st Qu.:      0   1st Qu.:      0  
##  Median :    0.0   Median :    0.0   Median :      0   Median :      0  
##  Mean   :  498.4   Mean   :   97.2   Mean   :   3019   Mean   :   4410  
##  3rd Qu.:    0.0   3rd Qu.:    0.0   3rd Qu.:      0   3rd Qu.:      0  
##  Max.   :49633.8   Max.   :76020.6   Max.   :1535990   Max.   :1679804  
##                                                                         
##  PlantasAma_SEMAR   PlantasMar_SEMAR   PlantiosAma_SEMAR PlantiosMar_SEMAR
##  Min.   :       0   Min.   :       0   Min.   :  0.000   Min.   :  0.000  
##  1st Qu.:       0   1st Qu.:       0   1st Qu.:  0.000   1st Qu.:  0.000  
##  Median :       0   Median :       0   Median :  0.000   Median :  0.000  
##  Mean   :   62638   Mean   :   72062   Mean   :  1.216   Mean   :  1.585  
##  3rd Qu.:       0   3rd Qu.:       0   3rd Qu.:  0.000   3rd Qu.:  0.000  
##  Max.   :30388390   Max.   :38743506   Max.   :607.000   Max.   :300.000  
##                                                                           
##   SemAma_SEMAR       SemMar_SEMAR          pf_1              pf_2        
##  Min.   :    0.00   Min.   : 0.0000   Min.   :  0.000   Min.   :  0.000  
##  1st Qu.:    0.00   1st Qu.: 0.0000   1st Qu.:  0.000   1st Qu.:  0.000  
##  Median :    0.00   Median : 0.0000   Median :  0.000   Median :  0.000  
##  Mean   :   61.44   Mean   : 0.2225   Mean   :  1.651   Mean   :  1.853  
##  3rd Qu.:    0.00   3rd Qu.: 0.0000   3rd Qu.:  0.000   3rd Qu.:  0.000  
##  Max.   :55615.00   Max.   :65.0000   Max.   :684.000   Max.   :773.000  
##                                                                          
##       pf_4                pf_5             pf_6              pf_7        
##  Min.   :    0.000   Min.   :   0.0   Min.   :  0.000   Min.   : 0.0000  
##  1st Qu.:    0.000   1st Qu.:   0.0   1st Qu.:  0.000   1st Qu.: 0.0000  
##  Median :    0.000   Median :   0.0   Median :  0.000   Median : 0.0000  
##  Mean   :   39.361   Mean   :  13.1   Mean   :  1.747   Mean   : 0.2882  
##  3rd Qu.:    0.159   3rd Qu.:   0.0   3rd Qu.:  0.000   3rd Qu.: 0.0000  
##  Max.   :23353.372   Max.   :1271.4   Max.   :129.176   Max.   :85.2828  
##                                                                          
##       pf_8              pf_9             pf_10             pf_11        
##  Min.   :    0.0   Min.   :   0.00   Min.   :   0.00   Min.   : 0.0000  
##  1st Qu.:    0.0   1st Qu.:   0.00   1st Qu.:   0.00   1st Qu.: 0.0000  
##  Median :    0.0   Median :   0.00   Median :   0.00   Median : 0.0000  
##  Mean   : 1110.5   Mean   :  17.06   Mean   :  21.62   Mean   : 0.4148  
##  3rd Qu.:  112.3   3rd Qu.:   0.00   3rd Qu.:   0.00   3rd Qu.: 0.0000  
##  Max.   :61605.7   Max.   :3212.79   Max.   :7873.58   Max.   :89.0000  
##                                                                         
##       gn_1               gn_2               gn_4               gn_6        
##  Min.   :0.000000   Min.   : 0.00000   Min.   :   0.000   Min.   : 0.0000  
##  1st Qu.:0.000000   1st Qu.: 0.00000   1st Qu.:   0.000   1st Qu.: 0.0000  
##  Median :0.000000   Median : 0.00000   Median :   0.000   Median : 0.0000  
##  Mean   :0.003788   Mean   : 0.02462   Mean   :   4.529   Mean   : 0.2393  
##  3rd Qu.:0.000000   3rd Qu.: 0.00000   3rd Qu.:   0.000   3rd Qu.: 0.0000  
##  Max.   :3.000000   Max.   :15.00000   Max.   :1408.128   Max.   :43.8430  
##                                                                            
##       gn_8              gn_9             gn_10              gn_11         
##  Min.   :   0.00   Min.   :   0.00   Min.   :   0.000   Min.   :0.000000  
##  1st Qu.:   0.00   1st Qu.:   0.00   1st Qu.:   0.000   1st Qu.:0.000000  
##  Median :   0.00   Median :   0.00   Median :   0.000   Median :0.000000  
##  Mean   :  69.42   Mean   :  12.26   Mean   :   2.991   Mean   :0.001894  
##  3rd Qu.:   0.00   3rd Qu.:   0.00   3rd Qu.:   0.000   3rd Qu.:0.000000  
##  Max.   :8648.12   Max.   :2005.21   Max.   :2598.805   Max.   :2.000000  
##                                                                           
##      gn_12             gn_13             pob_men           pob_wom       
##  Min.   : 0.0000   Min.   : 0.00000   Min.   : 222978   Min.   : 211340  
##  1st Qu.: 0.0000   1st Qu.: 0.00000   1st Qu.: 730338   1st Qu.: 753227  
##  Median : 0.0000   Median : 0.00000   Median :1296480   Median :1317132  
##  Mean   : 0.2735   Mean   : 0.09776   Mean   :1685349   Mean   :1752914  
##  3rd Qu.: 0.0000   3rd Qu.: 0.00000   3rd Qu.:1980471   3rd Qu.:2101332  
##  Max.   :82.4310   Max.   :28.67000   Max.   :8683082   Max.   :9089378  
##                                                                          
##       pob               homicr            hmenr            hwomenr       
##  Min.   :  434318   Min.   :  0.000   Min.   :  0.000   Min.   : 0.0000  
##  1st Qu.: 1482097   1st Qu.:  2.066   1st Qu.:  3.318   1st Qu.: 0.4069  
##  Median : 2613822   Median :  8.232   Median : 14.186   Median : 2.1389  
##  Mean   : 3438263   Mean   : 14.292   Mean   : 25.697   Mean   : 3.0404  
##  3rd Qu.: 4072705   3rd Qu.: 17.119   3rd Qu.: 31.067   3rd Qu.: 3.6877  
##  Max.   :17772460   Max.   :197.005   Max.   :347.275   Max.   :47.7861  
##                                                                          
##    hgunswomr         hgunsmenr          homiguns_r       heroin_average 
##  Min.   : 0.0000   Min.   :  0.0000   Min.   :  0.0000   Min.   :230.0  
##  1st Qu.: 0.0000   1st Qu.:  0.9412   1st Qu.:  0.5076   1st Qu.:265.0  
##  Median : 0.6018   Median :  6.9671   Median :  3.7233   Median :307.0  
##  Mean   : 1.4797   Mean   : 17.3331   Mean   :  9.3127   Mean   :320.3  
##  3rd Qu.: 1.5435   3rd Qu.: 20.3472   3rd Qu.: 10.7959   3rd Qu.:374.0  
##  Max.   :38.4259   Max.   :296.6719   Max.   :167.0961   Max.   :481.0  
##                                                          NA's   :64     
##  heroin_average_inflation_justed2 heroin_average_adjusted_purity
##  Min.   :286.0                    Min.   : 634.0                
##  1st Qu.:331.0                    1st Qu.: 768.0                
##  Median :394.0                    Median : 867.0                
##  Mean   :449.9                    Mean   : 910.4                
##  3rd Qu.:560.0                    3rd Qu.: 983.0                
##  Max.   :886.0                    Max.   :1561.0                
##  NA's   :64                       NA's   :64                    
##  heorin_average_adjusted_purity_i heroin_average_wholesale
##  Min.   : 788                     Min.   : 50750          
##  1st Qu.: 949                     1st Qu.: 57500          
##  Median :1077                     Median : 65500          
##  Mean   :1287                     Mean   : 86825          
##  3rd Qu.:1404                     3rd Qu.:129375          
##  Max.   :3091                     Max.   :162500          
##  NA's   :64                       NA's   :64              
##  heroin_average_inflation_adjuste cocaine_average_wholesale
##  Min.   : 53000                   Min.   :20500            
##  1st Qu.: 67770                   1st Qu.:26500            
##  Median : 86138                   Median :29000            
##  Mean   :129889                   Mean   :30711            
##  3rd Qu.:208621                   3rd Qu.:32550            
##  Max.   :321782                   Max.   :48300            
##  NA's   :64                       NA's   :32               
##  cocaine_average_wholesale_inflat cocaine_average 
##  Min.   :26798                    Min.   : 64.00  
##  1st Qu.:30848                    1st Qu.: 72.00  
##  Median :33612                    Median : 77.00  
##  Mean   :43667                    Mean   : 83.75  
##  3rd Qu.:51537                    3rd Qu.: 96.50  
##  Max.   :95643                    Max.   :120.00  
##  NA's   :64                       NA's   :32      
##  cocaine_average_inflation_adjust cocaine_average_adjusted_purity
##  Min.   : 82.0                    Min.   : 93.0                  
##  1st Qu.: 99.0                    1st Qu.:108.0                  
##  Median :109.0                    Median :127.0                  
##  Mean   :110.8                    Mean   :141.9                  
##  3rd Qu.:120.0                    3rd Qu.:178.0                  
##  Max.   :158.0                    Max.   :221.0                  
##  NA's   :64                       NA's   :64                     
##  cocaine_average_adjusted_purity_     _merge          year          
##  Min.   :118.0                    Min.   :1.000   Length:1056       
##  1st Qu.:165.0                    1st Qu.:3.000   Class :character  
##  Median :181.0                    Median :3.000   Mode  :character  
##  Mean   :185.5                    Mean   :2.939                     
##  3rd Qu.:213.0                    3rd Qu.:3.000                     
##  Max.   :274.0                    Max.   :3.000                     
##  NA's   :64                                                         
##    incocaina           inheroin       carte_trasiegoC    carte_trasiegoH   
##  Min.   :    0.00   Min.   :  0.000   Length:1056        Length:1056       
##  1st Qu.:    0.00   1st Qu.:  0.000   Class :character   Class :character  
##  Median :    0.09   Median :  0.000   Mode  :character   Mode  :character  
##  Mean   :  240.95   Mean   :  5.015                                        
##  3rd Qu.:   26.53   3rd Qu.:  0.000                                        
##  Max.   :46718.38   Max.   :340.560                                        
## 
attach(base)
h1 <- ggplot(base, aes(x=homicr))
h1 + geom_histogram()
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.

h2 <- ggplot(base, aes(log(x=homicr)))
h2 + geom_histogram()
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## Warning: Removed 153 rows containing non-finite values (`stat_bin()`).

h3 <- ggplot(base, aes(x=homiguns_r))
h3 + geom_histogram()
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.

h4<- ggplot(base, aes(log(x=homiguns_r)))
h4 + geom_histogram()
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## Warning: Removed 168 rows containing non-finite values (`stat_bin()`).

h5<- ggplot(base, aes(x=heroin_average_wholesale))
h5 + geom_histogram()
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## Warning: Removed 64 rows containing non-finite values (`stat_bin()`).

h6<- ggplot(base, aes(log(x=heroin_average_wholesale)))
h6 + geom_histogram()
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## Warning: Removed 64 rows containing non-finite values (`stat_bin()`).

h7<- ggplot(base, aes(x=inheroin))
h7 + geom_histogram()
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.

h8<- ggplot(base, aes(log(x=inheroin)))
h8 + geom_histogram()
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## Warning: Removed 822 rows containing non-finite values (`stat_bin()`).

Correlación entre las variables de interés númericas

plot(inheroin, homicr , pch = 19, col = "lightblue")
abline(lm(inheroin ~ homicr), col = "red", lwd = 3)

text(paste("Correlación:", round(cor(inheroin, homicr), 2)), x = 50, y = 50)

plot(inheroin, homiguns_r , pch = 19, col = "lightblue")
abline(lm(inheroin ~ homiguns_r), col = "red", lwd = 3)

text(paste("Correlación:", round(cor(inheroin, homiguns_r), 2)), x = 50, y = 50)

plot(heroin_average_wholesale*inheroin, homicr , pch = 19, col = "lightblue")
abline(lm(heroin_average_wholesale*inheroin ~ homicr), col = "red", lwd = 3)

text(paste("Correlación:", round(cor(heroin_average_wholesale*inheroin, homicr), 2)), x = 100, y = 100)

Análisis exploratorio de las variables de interés

ggplot(base, aes(x = anio_ocur, y = homicr)) +
 geom_point(fill = rgb(0, 0.5, 1, alpha = 1)) + 
  labs(title = "Tasa de Homicidios en México",
       subtitle = "1990-2022",
       caption = "El calculo responde al total de homicidios ocurridos por armas de fuego durante el año t, entre la población total durante el año t por cien mil habitantes",
       tag = "Fig. 1")+
theme(plot.caption.position = "plot",
        plot.caption = element_text(hjust = 0)) 

La violencia a través de los homicidios incrementa a partir de 1997, ¿qué sucedió en esos años?

ggplot(base, aes(x = anio_ocur, y =homicr , color = cve_ent)) +
  geom_area(show.legend = FALSE) +
  facet_wrap(~nom_ent , scales = "free") +
  theme(strip.text = element_text(size = 6),
        strip.background = element_blank()) +
  labs(title = "Tasa de Homicidios por entidad federativa",
       subtitle = "1990-2022",
       caption = "El cálculo responde al total de homicidios ocurridos por armas de fuego durante el año t, entre la población total durante el año t por cien mil habitantes",
       tag = "Fig. 2")+
theme(plot.caption.position = "plot",
        plot.caption = element_text(hjust = 0)) 

Los niveles de violencia se generalizo en las entidades federativas, claramente un otras con mayor intensidad. Los homicidios responden en la entidad ocurrida NO en la registrada.

str(base)
## tibble [1,056 × 97] (S3: tbl_df/tbl/data.frame)
##  $ anio_ocur                       : num [1:1056] 1990 1990 1990 1990 1990 1990 1990 1990 1990 1990 ...
##   ..- attr(*, "label")= chr "ANIO_OCUR"
##   ..- attr(*, "format.stata")= chr "%8.0g"
##  $ cve_ent                         : num [1:1056] 22 2 9 8 21 4 5 1 31 19 ...
##   ..- attr(*, "label")= chr "CVE_ENT"
##   ..- attr(*, "format.stata")= chr "%8.0g"
##  $ ent                             : chr [1:1056] "22" "2" "9" "8" ...
##   ..- attr(*, "label")= chr "CVE_ENT"
##   ..- attr(*, "format.stata")= chr "%9s"
##  $ nom_ent                         : chr [1:1056] "Querétaro" "Baja California" "Ciudad de México" "Chihuahua" ...
##   ..- attr(*, "label")= chr "NOM_ENT"
##   ..- attr(*, "format.stata")= chr "%31s"
##  $ homicide                        : num [1:1056] 0 2 0 3 0 0 0 0 0 0 ...
##   ..- attr(*, "label")= chr "(sum) homicide"
##   ..- attr(*, "format.stata")= chr "%9.0g"
##  $ homicide_man                    : num [1:1056] 0 2 0 3 0 0 0 0 0 0 ...
##   ..- attr(*, "label")= chr "(sum) homicide_man"
##   ..- attr(*, "format.stata")= chr "%9.0g"
##  $ homicide_woman                  : num [1:1056] 0 0 0 0 0 0 0 0 0 0 ...
##   ..- attr(*, "label")= chr "(sum) homicide_woman"
##   ..- attr(*, "format.stata")= chr "%9.0g"
##  $ homiguns                        : num [1:1056] 0 1 0 2 0 0 0 0 0 0 ...
##   ..- attr(*, "label")= chr "(sum) homiguns"
##   ..- attr(*, "format.stata")= chr "%9.0g"
##  $ homigunsman                     : num [1:1056] 0 1 0 2 0 0 0 0 0 0 ...
##   ..- attr(*, "label")= chr "(sum) homigunsman"
##   ..- attr(*, "format.stata")= chr "%9.0g"
##  $ homigunswoman                   : num [1:1056] 0 0 0 0 0 0 0 0 0 0 ...
##   ..- attr(*, "label")= chr "(sum) homigunswoman"
##   ..- attr(*, "format.stata")= chr "%9.0g"
##  $ AmpFen_SEDENA                   : num [1:1056] 0 0 0 0 0 0 0 0 0 0 ...
##   ..- attr(*, "label")= chr "(sum) AmpFen_SEDENA"
##   ..- attr(*, "format.stata")= chr "%9.0g"
##  $ AseFen_SEDENA                   : num [1:1056] 0 0 0 0 0 0 0 0 0 0 ...
##   ..- attr(*, "label")= chr "(sum) AseFen_SEDENA"
##   ..- attr(*, "format.stata")= chr "%9.0g"
##  $ AseCoc_SEDENA                   : num [1:1056] 0 0 0 0 0 0 0 0 0 0 ...
##   ..- attr(*, "label")= chr "(sum) AseCoc_SEDENA"
##   ..- attr(*, "format.stata")= chr "%9.0g"
##  $ AseGomOpio_SEDENA               : num [1:1056] 0 0 0 2.27 0 ...
##   ..- attr(*, "label")= chr "(sum) AseGomOpio_SEDENA"
##   ..- attr(*, "format.stata")= chr "%9.0g"
##  $ AseHer_SEDENA                   : num [1:1056] 0 0 0 0 0 0 0 0 0 0 ...
##   ..- attr(*, "label")= chr "(sum) AseHer_SEDENA"
##   ..- attr(*, "format.stata")= chr "%9.0g"
##  $ AseMar_SEDENA                   : num [1:1056] 0 0 0 100 590 ...
##   ..- attr(*, "label")= chr "(sum) AseMar_SEDENA"
##   ..- attr(*, "format.stata")= chr "%9.0g"
##  $ AseMet_SEDENA                   : num [1:1056] 0 0 0 0 0 0 0 0 0 0 ...
##   ..- attr(*, "label")= chr "(sum) AseMet_SEDENA"
##   ..- attr(*, "format.stata")= chr "%9.0g"
##  $ HecAma_fum_SEDENA               : num [1:1056] 0 0 0 0 0 0 0 0 0 0 ...
##   ..- attr(*, "label")= chr "(sum) HecAma_fum_SEDENA"
##   ..- attr(*, "format.stata")= chr "%9.0g"
##  $ HecAma_man_SEDENA               : num [1:1056] 0 0 0 829.06 1.32 ...
##   ..- attr(*, "label")= chr "(sum) HecAma_man_SEDENA"
##   ..- attr(*, "format.stata")= chr "%9.0g"
##  $ HecMar_fum_SEDENA               : num [1:1056] 0 0 0 0 0 0 0 0 0 0 ...
##   ..- attr(*, "label")= chr "(sum) HecMar_fum_SEDENA"
##   ..- attr(*, "format.stata")= chr "%9.0g"
##  $ HecMar_man_SEDENA               : num [1:1056] 0 37.84 0 786.81 8.98 ...
##   ..- attr(*, "label")= chr "(sum) HecMar_man_SEDENA"
##   ..- attr(*, "format.stata")= chr "%9.0g"
##  $ IncCoc_SEDENA                   : num [1:1056] 0 0 0 0 0 0 0 0 0 0 ...
##   ..- attr(*, "label")= chr "(sum) IncCoc_SEDENA"
##   ..- attr(*, "format.stata")= chr "%9.0g"
##  $ IncGomOpio_SEDENA               : num [1:1056] 0 0 0 0 0 0 0 0 0 0 ...
##   ..- attr(*, "label")= chr "(sum) IncGomOpio_SEDENA"
##   ..- attr(*, "format.stata")= chr "%9.0g"
##  $ IncHer_SEDENA                   : num [1:1056] 0 0 0 0 0 0 0 0 0 0 ...
##   ..- attr(*, "label")= chr "(sum) IncHer_SEDENA"
##   ..- attr(*, "format.stata")= chr "%9.0g"
##  $ IncMar_SEDENA                   : num [1:1056] 0 0 0 4818 155 ...
##   ..- attr(*, "label")= chr "(sum) IncMar_SEDENA"
##   ..- attr(*, "format.stata")= chr "%9.0g"
##  $ IncMet_SEDENA                   : num [1:1056] 0 0 0 0 0 0 0 0 0 0 ...
##   ..- attr(*, "label")= chr "(sum) IncMet_SEDENA"
##   ..- attr(*, "format.stata")= chr "%9.0g"
##  $ IncSemAma_SEDENA                : num [1:1056] 0 0 0 35 0 ...
##   ..- attr(*, "label")= chr "(sum) IncSemAma_SEDENA"
##   ..- attr(*, "format.stata")= chr "%9.0g"
##  $ IncSemMar_SEDENA                : num [1:1056] 0 0 0 50.3 0.6 ...
##   ..- attr(*, "label")= chr "(sum) IncSemMar_SEDENA"
##   ..- attr(*, "format.stata")= chr "%9.0g"
##  $ LabCoc_SEDENA                   : num [1:1056] 0 0 0 0 0 0 0 0 0 0 ...
##   ..- attr(*, "label")= chr "(sum) LabCoc_SEDENA"
##   ..- attr(*, "format.stata")= chr "%9.0g"
##  $ LabHer_SEDENA                   : num [1:1056] 0 0 0 0 0 0 0 0 0 0 ...
##   ..- attr(*, "label")= chr "(sum) LabHer_SEDENA"
##   ..- attr(*, "format.stata")= chr "%9.0g"
##  $ LabMet_SEDENA                   : num [1:1056] 0 0 0 0 0 0 0 0 0 0 ...
##   ..- attr(*, "label")= chr "(sum) LabMet_SEDENA"
##   ..- attr(*, "format.stata")= chr "%9.0g"
##  $ PasFen_SEDENA                   : num [1:1056] 0 0 0 0 0 0 0 0 0 0 ...
##   ..- attr(*, "label")= chr "(sum) PasFen_SEDENA"
##   ..- attr(*, "format.stata")= chr "%9.0g"
##  $ PlaAma_fum_SEDENA               : num [1:1056] 0 0 0 0 0 0 0 0 0 0 ...
##   ..- attr(*, "label")= chr "(sum) PlaAma_fum_SEDENA"
##   ..- attr(*, "format.stata")= chr "%9.0g"
##  $ PlaAma_man_SEDENA               : num [1:1056] 0 0 0 10340 28 ...
##   ..- attr(*, "label")= chr "(sum) PlaAma_man_SEDENA"
##   ..- attr(*, "format.stata")= chr "%9.0g"
##  $ PlaMar_fum_SEDENA               : num [1:1056] 0 0 0 0 0 0 0 0 0 0 ...
##   ..- attr(*, "label")= chr "(sum) PlaMar_fum_SEDENA"
##   ..- attr(*, "format.stata")= chr "%9.0g"
##  $ PlaMar_man_SEDENA               : num [1:1056] 0 38 0 11468 240 ...
##   ..- attr(*, "label")= chr "(sum) PlaMar_man_SEDENA"
##   ..- attr(*, "format.stata")= chr "%9.0g"
##  $ SemAma_SEDENA                   : num [1:1056] 0 0 0 16.32 0.43 ...
##   ..- attr(*, "label")= chr "(sum) SemAma_SEDENA"
##   ..- attr(*, "format.stata")= chr "%9.0g"
##  $ SemMar_SEDENA                   : num [1:1056] 0 0.2 0 15.4 12.7 ...
##   ..- attr(*, "label")= chr "(sum) SemMar_SEDENA"
##   ..- attr(*, "format.stata")= chr "%9.0g"
##  $ AseCoc_SEMAR                    : num [1:1056] 0 0 0 0 0 0 0 0 0 0 ...
##   ..- attr(*, "label")= chr "(sum) AseCoc_SEMAR"
##   ..- attr(*, "format.stata")= chr "%9.0g"
##  $ AseHer_SEMAR                    : num [1:1056] 0 0 0 0 0 0 0 0 0 0 ...
##   ..- attr(*, "label")= chr "(sum) AseHer_SEMAR"
##   ..- attr(*, "format.stata")= chr "%9.0g"
##  $ AseMar_SEMAR                    : num [1:1056] 0 0 0 0 0 0 0 0 0 0 ...
##   ..- attr(*, "label")= chr "(sum) AseMar_SEMAR"
##   ..- attr(*, "format.stata")= chr "%9.0g"
##  $ AseMet_SEMAR                    : num [1:1056] 0 0 0 0 0 0 0 0 0 0 ...
##   ..- attr(*, "label")= chr "(sum) AseMet_SEMAR"
##   ..- attr(*, "format.stata")= chr "%9.0g"
##  $ M2Ama_SEMAR                     : num [1:1056] 0 0 0 0 0 0 0 0 0 0 ...
##   ..- attr(*, "label")= chr "(sum) M2Ama_SEMAR"
##   ..- attr(*, "format.stata")= chr "%9.0g"
##  $ M2Mar_SEMAR                     : num [1:1056] 0 0 0 0 0 0 0 0 0 0 ...
##   ..- attr(*, "label")= chr "(sum) M2Mar_SEMAR"
##   ..- attr(*, "format.stata")= chr "%9.0g"
##  $ PlantasAma_SEMAR                : num [1:1056] 0 0 0 0 0 0 0 0 0 0 ...
##   ..- attr(*, "label")= chr "(sum) PlantasAma_SEMAR"
##   ..- attr(*, "format.stata")= chr "%9.0g"
##  $ PlantasMar_SEMAR                : num [1:1056] 0 0 0 0 0 0 0 0 0 0 ...
##   ..- attr(*, "label")= chr "(sum) PlantasMar_SEMAR"
##   ..- attr(*, "format.stata")= chr "%9.0g"
##  $ PlantiosAma_SEMAR               : num [1:1056] 0 0 0 0 0 0 0 0 0 0 ...
##   ..- attr(*, "label")= chr "(sum) PlantiosAma_SEMAR"
##   ..- attr(*, "format.stata")= chr "%9.0g"
##  $ PlantiosMar_SEMAR               : num [1:1056] 0 0 0 0 0 0 0 0 0 0 ...
##   ..- attr(*, "label")= chr "(sum) PlantiosMar_SEMAR"
##   ..- attr(*, "format.stata")= chr "%9.0g"
##  $ SemAma_SEMAR                    : num [1:1056] 0 0 0 0 0 0 0 0 0 0 ...
##   ..- attr(*, "label")= chr "(sum) SemAma_SEMAR"
##   ..- attr(*, "format.stata")= chr "%9.0g"
##  $ SemMar_SEMAR                    : num [1:1056] 0 0 0 0 0 0 0 0 0 0 ...
##   ..- attr(*, "label")= chr "(sum) SemMar_SEMAR"
##   ..- attr(*, "format.stata")= chr "%9.0g"
##  $ pf_1                            : num [1:1056] 0 0 0 0 0 0 0 0 0 0 ...
##   ..- attr(*, "label")= chr "(sum) pf_1"
##   ..- attr(*, "format.stata")= chr "%9.0g"
##  $ pf_2                            : num [1:1056] 0 0 0 0 0 0 0 0 0 0 ...
##   ..- attr(*, "label")= chr "(sum) pf_2"
##   ..- attr(*, "format.stata")= chr "%9.0g"
##  $ pf_4                            : num [1:1056] 0 0 0 0 0 0 0 0 0 0 ...
##   ..- attr(*, "label")= chr "(sum) pf_4"
##   ..- attr(*, "format.stata")= chr "%9.0g"
##  $ pf_5                            : num [1:1056] 0 0 0 0 0 0 0 0 0 0 ...
##   ..- attr(*, "label")= chr "(sum) pf_5"
##   ..- attr(*, "format.stata")= chr "%9.0g"
##  $ pf_6                            : num [1:1056] 0 0 0 0 0 0 0 0 0 0 ...
##   ..- attr(*, "label")= chr "(sum) pf_6"
##   ..- attr(*, "format.stata")= chr "%9.0g"
##  $ pf_7                            : num [1:1056] 0 0 0 0 0 0 0 0 0 0 ...
##   ..- attr(*, "label")= chr "(sum) pf_7"
##   ..- attr(*, "format.stata")= chr "%9.0g"
##  $ pf_8                            : num [1:1056] 0 0 0 0 0 0 0 0 0 0 ...
##   ..- attr(*, "label")= chr "(sum) pf_8"
##   ..- attr(*, "format.stata")= chr "%9.0g"
##  $ pf_9                            : num [1:1056] 0 0 0 0 0 0 0 0 0 0 ...
##   ..- attr(*, "label")= chr "(sum) pf_9"
##   ..- attr(*, "format.stata")= chr "%9.0g"
##  $ pf_10                           : num [1:1056] 0 0 0 0 0 0 0 0 0 0 ...
##   ..- attr(*, "label")= chr "(sum) pf_10"
##   ..- attr(*, "format.stata")= chr "%9.0g"
##  $ pf_11                           : num [1:1056] 0 0 0 0 0 0 0 0 0 0 ...
##   ..- attr(*, "label")= chr "(sum) pf_11"
##   ..- attr(*, "format.stata")= chr "%9.0g"
##  $ gn_1                            : num [1:1056] 0 0 0 0 0 0 0 0 0 0 ...
##   ..- attr(*, "label")= chr "(sum) gn_1"
##   ..- attr(*, "format.stata")= chr "%9.0g"
##  $ gn_2                            : num [1:1056] 0 0 0 0 0 0 0 0 0 0 ...
##   ..- attr(*, "label")= chr "(sum) gn_2"
##   ..- attr(*, "format.stata")= chr "%9.0g"
##  $ gn_4                            : num [1:1056] 0 0 0 0 0 0 0 0 0 0 ...
##   ..- attr(*, "label")= chr "(sum) gn_4"
##   ..- attr(*, "format.stata")= chr "%9.0g"
##  $ gn_6                            : num [1:1056] 0 0 0 0 0 0 0 0 0 0 ...
##   ..- attr(*, "label")= chr "(sum) gn_6"
##   ..- attr(*, "format.stata")= chr "%9.0g"
##  $ gn_8                            : num [1:1056] 0 0 0 0 0 0 0 0 0 0 ...
##   ..- attr(*, "label")= chr "(sum) gn_8"
##   ..- attr(*, "format.stata")= chr "%9.0g"
##  $ gn_9                            : num [1:1056] 0 0 0 0 0 0 0 0 0 0 ...
##   ..- attr(*, "label")= chr "(sum) gn_9"
##   ..- attr(*, "format.stata")= chr "%9.0g"
##  $ gn_10                           : num [1:1056] 0 0 0 0 0 0 0 0 0 0 ...
##   ..- attr(*, "label")= chr "(sum) gn_10"
##   ..- attr(*, "format.stata")= chr "%9.0g"
##  $ gn_11                           : num [1:1056] 0 0 0 0 0 0 0 0 0 0 ...
##   ..- attr(*, "label")= chr "(sum) gn_11"
##   ..- attr(*, "format.stata")= chr "%9.0g"
##  $ gn_12                           : num [1:1056] 0 0 0 0 0 0 0 0 0 0 ...
##   ..- attr(*, "label")= chr "(sum) gn_12"
##   ..- attr(*, "format.stata")= chr "%9.0g"
##  $ gn_13                           : num [1:1056] 0 0 0 0 0 0 0 0 0 0 ...
##   ..- attr(*, "label")= chr "(sum) gn_13"
##   ..- attr(*, "format.stata")= chr "%9.0g"
##  $ pob_men                         : num [1:1056] 700483 1264712 4156737 1542440 2508433 ...
##   ..- attr(*, "label")= chr "(sum) pob_men"
##   ..- attr(*, "format.stata")= chr "%9.0g"
##  $ pob_wom                         : num [1:1056] 734614 1228963 4484256 1535841 2661493 ...
##   ..- attr(*, "label")= chr "(sum) pob_wom"
##   ..- attr(*, "format.stata")= chr "%9.0g"
##  $ pob                             : num [1:1056] 1435097 2493675 8640993 3078281 5169926 ...
##   ..- attr(*, "label")= chr "(sum) pob"
##   ..- attr(*, "format.stata")= chr "%9.0g"
##  $ homicr                          : num [1:1056] 0 0.0802 0 0.0975 0 ...
##   ..- attr(*, "format.stata")= chr "%9.0g"
##  $ hmenr                           : num [1:1056] 0 0.158 0 0.194 0 ...
##   ..- attr(*, "format.stata")= chr "%9.0g"
##  $ hwomenr                         : num [1:1056] 0 0 0 0 0 0 0 0 0 0 ...
##   ..- attr(*, "format.stata")= chr "%9.0g"
##  $ hgunswomr                       : num [1:1056] 0 0 0 0 0 0 0 0 0 0 ...
##   ..- attr(*, "format.stata")= chr "%9.0g"
##  $ hgunsmenr                       : num [1:1056] 0 0.0791 0 0.1297 0 ...
##   ..- attr(*, "format.stata")= chr "%9.0g"
##  $ homiguns_r                      : num [1:1056] 0 0.0401 0 0.065 0 ...
##   ..- attr(*, "format.stata")= chr "%9.0g"
##  $ heroin_average                  : num [1:1056] 359 359 359 359 359 359 359 359 359 359 ...
##   ..- attr(*, "format.stata")= chr "%8.0g"
##  $ heroin_average_inflation_justed2: num [1:1056] 711 711 711 711 711 711 711 711 711 711 ...
##   ..- attr(*, "label")= chr "heroin_average_inflation _justed2020"
##   ..- attr(*, "format.stata")= chr "%8.0g"
##  $ heroin_average_adjusted_purity  : num [1:1056] 1561 1561 1561 1561 1561 ...
##   ..- attr(*, "format.stata")= chr "%8.0g"
##  $ heorin_average_adjusted_purity_i: num [1:1056] 3091 3091 3091 3091 3091 ...
##   ..- attr(*, "label")= chr "Heorin_average_adjusted_purity_inflation"
##   ..- attr(*, "format.stata")= chr "%8.0g"
##  $ heroin_average_wholesale        : num [1:1056] 162500 162500 162500 162500 162500 ...
##   ..- attr(*, "format.stata")= chr "%8.0g"
##  $ heroin_average_inflation_adjuste: num [1:1056] 321782 321782 321782 321782 321782 ...
##   ..- attr(*, "label")= chr "heroin_average_inflation_adjusted_wholesale"
##   ..- attr(*, "format.stata")= chr "%8.0g"
##  $ cocaine_average_wholesale       : num [1:1056] 48300 48300 48300 48300 48300 48300 48300 48300 48300 48300 ...
##   ..- attr(*, "format.stata")= chr "%8.0g"
##  $ cocaine_average_wholesale_inflat: num [1:1056] 95643 95643 95643 95643 95643 ...
##   ..- attr(*, "label")= chr "cocaine_average_wholesale_inflation_adjusted"
##   ..- attr(*, "format.stata")= chr "%8.0g"
##  $ cocaine_average                 : num [1:1056] 80 80 80 80 80 80 80 80 80 80 ...
##   ..- attr(*, "format.stata")= chr "%8.0g"
##  $ cocaine_average_inflation_adjust: num [1:1056] 158 158 158 158 158 158 158 158 158 158 ...
##   ..- attr(*, "label")= chr "cocaine_average_inflation_adjusted"
##   ..- attr(*, "format.stata")= chr "%8.0g"
##  $ cocaine_average_adjusted_purity : num [1:1056] 140 140 140 140 140 140 140 140 140 140 ...
##   ..- attr(*, "format.stata")= chr "%8.0g"
##  $ cocaine_average_adjusted_purity_: num [1:1056] 274 274 274 274 274 274 274 274 274 274 ...
##   ..- attr(*, "label")= chr "cocaine_average_adjusted_purity_inflation"
##   ..- attr(*, "format.stata")= chr "%8.0g"
##  $ _merge                          : dbl+lbl [1:1056] 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3,...
##    ..@ format.stata: chr "%23.0g"
##    ..@ labels      : Named num [1:5] 1 2 3 4 5
##    .. ..- attr(*, "names")= chr [1:5] "master only (1)" "using only (2)" "matched (3)" "missing updated (4)" ...
##  $ year                            : chr [1:1056] "1990" "1990" "1990" "1990" ...
##   ..- attr(*, "format.stata")= chr "%9s"
##  $ incocaina                       : num [1:1056] 0 0 0 0 0 0 0 0 0 0 ...
##   ..- attr(*, "format.stata")= chr "%9.0g"
##  $ inheroin                        : num [1:1056] 0 0 0 0 0 0 0 0 0 0 ...
##   ..- attr(*, "format.stata")= chr "%9.0g"
##  $ carte_trasiegoC                 : chr [1:1056] "0" "0" "0" "0" ...
##   ..- attr(*, "format.stata")= chr "%9s"
##  $ carte_trasiegoH                 : chr [1:1056] "0" "0" "0" "0" ...
##   ..- attr(*, "format.stata")= chr "%9s"
summary(object = base)
##    anio_ocur       cve_ent          ent              nom_ent         
##  Min.   :1990   Min.   : 1.00   Length:1056        Length:1056       
##  1st Qu.:1998   1st Qu.: 8.75   Class :character   Class :character  
##  Median :2006   Median :16.50   Mode  :character   Mode  :character  
##  Mean   :2006   Mean   :16.50                                        
##  3rd Qu.:2014   3rd Qu.:24.25                                        
##  Max.   :2022   Max.   :32.00                                        
##                                                                      
##     homicide        homicide_man    homicide_woman       homiguns     
##  Min.   :   0.00   Min.   :   0.0   Min.   :   0.00   Min.   :   0.0  
##  1st Qu.:  26.75   1st Qu.:  22.0   1st Qu.:   3.00   1st Qu.:   8.0  
##  Median : 176.00   Median : 156.5   Median :  23.00   Median :  93.0  
##  Mean   : 509.13   Mean   : 449.3   Mean   :  56.78   Mean   : 327.6  
##  3rd Qu.: 635.00   3rd Qu.: 568.5   3rd Qu.:  68.00   3rd Qu.: 410.2  
##  Max.   :7886.00   Max.   :6362.0   Max.   :1056.00   Max.   :6004.0  
##                                                                       
##   homigunsman     homigunswoman    AmpFen_SEDENA      AseFen_SEDENA   
##  Min.   :   0.0   Min.   :  0.00   Min.   :  0.0000   Min.   :  0.00  
##  1st Qu.:   7.0   1st Qu.:  0.00   1st Qu.:  0.0000   1st Qu.:  0.00  
##  Median :  86.0   Median :  7.00   Median :  0.0000   Median :  0.00  
##  Mean   : 300.1   Mean   : 26.89   Mean   :  0.6903   Mean   :  1.37  
##  3rd Qu.: 378.5   3rd Qu.: 29.25   3rd Qu.:  0.0000   3rd Qu.:  0.00  
##  Max.   :5188.0   Max.   :804.00   Max.   :521.0000   Max.   :286.05  
##                                                                       
##  AseCoc_SEDENA      AseGomOpio_SEDENA AseHer_SEDENA    AseMar_SEDENA      
##  Min.   :   0.000   Min.   :  0.000   Min.   :  0.00   Min.   :     0.00  
##  1st Qu.:   0.000   1st Qu.:  0.000   1st Qu.:  0.00   1st Qu.:     0.00  
##  Median :   0.000   Median :  0.000   Median :  0.00   Median :    29.98  
##  Mean   :  60.187   Mean   :  8.254   Mean   :  2.97   Mean   :  5339.53  
##  3rd Qu.:   1.055   3rd Qu.:  0.000   3rd Qu.:  0.00   3rd Qu.:  2176.59  
##  Max.   :4711.415   Max.   :995.399   Max.   :280.74   Max.   :275652.79  
##                                                                           
##  AseMet_SEDENA     HecAma_fum_SEDENA HecAma_man_SEDENA  HecMar_fum_SEDENA
##  Min.   :    0.0   Min.   :   0.00   Min.   :   0.000   Min.   :   0.0   
##  1st Qu.:    0.0   1st Qu.:   0.00   1st Qu.:   0.000   1st Qu.:   0.0   
##  Median :    0.0   Median :   0.00   Median :   0.000   Median :   0.0   
##  Mean   :  191.5   Mean   :  55.95   Mean   : 338.199   Mean   :  61.6   
##  3rd Qu.:    0.0   3rd Qu.:   0.00   3rd Qu.:   9.056   3rd Qu.:   0.0   
##  Max.   :29675.9   Max.   :2178.34   Max.   :9494.069   Max.   :3594.7   
##                                                                          
##  HecMar_man_SEDENA  IncCoc_SEDENA    IncGomOpio_SEDENA IncHer_SEDENA     
##  Min.   :   0.000   Min.   : 0.000   Min.   : 0.0000   Min.   :0.000000  
##  1st Qu.:   0.000   1st Qu.: 0.000   1st Qu.: 0.0000   1st Qu.:0.000000  
##  Median :   3.703   Median : 0.000   Median : 0.0000   Median :0.000000  
##  Mean   : 348.035   Mean   : 0.182   Mean   : 0.2108   Mean   :0.003922  
##  3rd Qu.: 161.418   3rd Qu.: 0.000   3rd Qu.: 0.0000   3rd Qu.:0.000000  
##  Max.   :8012.398   Max.   :70.765   Max.   :17.5880   Max.   :2.252000  
##                                                                          
##  IncMar_SEDENA      IncMet_SEDENA       IncSemAma_SEDENA  IncSemMar_SEDENA 
##  Min.   :     0.0   Min.   :0.0000000   Min.   :   0.00   Min.   :   0.00  
##  1st Qu.:     0.0   1st Qu.:0.0000000   1st Qu.:   0.00   1st Qu.:   0.00  
##  Median :    11.5   Median :0.0000000   Median :   0.00   Median :   0.00  
##  Mean   : 14978.3   Mean   :0.0005115   Mean   :  30.89   Mean   : 144.63  
##  3rd Qu.:  3173.8   3rd Qu.:0.0000000   3rd Qu.:   1.00   3rd Qu.:  30.24  
##  Max.   :623229.3   Max.   :0.1100000   Max.   :2118.42   Max.   :6280.11  
##                                                                            
##  LabCoc_SEDENA      LabHer_SEDENA     LabMet_SEDENA     PasFen_SEDENA    
##  Min.   :0.000000   Min.   :0.00000   Min.   :  0.000   Min.   :      0  
##  1st Qu.:0.000000   1st Qu.:0.00000   1st Qu.:  0.000   1st Qu.:      0  
##  Median :0.000000   Median :0.00000   Median :  0.000   Median :      0  
##  Mean   :0.000947   Mean   :0.01989   Mean   :  1.959   Mean   :   7174  
##  3rd Qu.:0.000000   3rd Qu.:0.00000   3rd Qu.:  0.000   3rd Qu.:      0  
##  Max.   :1.000000   Max.   :3.00000   Max.   :350.000   Max.   :2450350  
##                                                                          
##  PlaAma_fum_SEDENA PlaAma_man_SEDENA   PlaMar_fum_SEDENA PlaMar_man_SEDENA
##  Min.   :    0.0   Min.   :     0.00   Min.   :    0.0   Min.   :    0    
##  1st Qu.:    0.0   1st Qu.:     0.00   1st Qu.:    0.0   1st Qu.:    0    
##  Median :    0.0   Median :     0.00   Median :    0.0   Median :   31    
##  Mean   :  380.2   Mean   :  2942.79   Mean   :  490.5   Mean   : 3690    
##  3rd Qu.:    0.0   3rd Qu.:    91.25   3rd Qu.:    0.0   3rd Qu.: 1441    
##  Max.   :19514.0   Max.   :121011.00   Max.   :24691.0   Max.   :91882    
##                                                                           
##  SemAma_SEDENA      SemMar_SEDENA      AseCoc_SEMAR      AseHer_SEMAR     
##  Min.   :   0.000   Min.   :   0.00   Min.   :    0.0   Min.   : 0.00000  
##  1st Qu.:   0.000   1st Qu.:   0.00   1st Qu.:    0.0   1st Qu.: 0.00000  
##  Median :   0.000   Median :   0.00   Median :    0.0   Median : 0.00000  
##  Mean   :   6.953   Mean   :  25.49   Mean   :  136.7   Mean   : 0.05541  
##  3rd Qu.:   0.000   3rd Qu.:   5.00   3rd Qu.:    0.0   3rd Qu.: 0.00000  
##  Max.   :1122.052   Max.   :2111.09   Max.   :23365.0   Max.   :33.80580  
##                                                                           
##   AseMar_SEMAR      AseMet_SEMAR      M2Ama_SEMAR       M2Mar_SEMAR     
##  Min.   :    0.0   Min.   :    0.0   Min.   :      0   Min.   :      0  
##  1st Qu.:    0.0   1st Qu.:    0.0   1st Qu.:      0   1st Qu.:      0  
##  Median :    0.0   Median :    0.0   Median :      0   Median :      0  
##  Mean   :  498.4   Mean   :   97.2   Mean   :   3019   Mean   :   4410  
##  3rd Qu.:    0.0   3rd Qu.:    0.0   3rd Qu.:      0   3rd Qu.:      0  
##  Max.   :49633.8   Max.   :76020.6   Max.   :1535990   Max.   :1679804  
##                                                                         
##  PlantasAma_SEMAR   PlantasMar_SEMAR   PlantiosAma_SEMAR PlantiosMar_SEMAR
##  Min.   :       0   Min.   :       0   Min.   :  0.000   Min.   :  0.000  
##  1st Qu.:       0   1st Qu.:       0   1st Qu.:  0.000   1st Qu.:  0.000  
##  Median :       0   Median :       0   Median :  0.000   Median :  0.000  
##  Mean   :   62638   Mean   :   72062   Mean   :  1.216   Mean   :  1.585  
##  3rd Qu.:       0   3rd Qu.:       0   3rd Qu.:  0.000   3rd Qu.:  0.000  
##  Max.   :30388390   Max.   :38743506   Max.   :607.000   Max.   :300.000  
##                                                                           
##   SemAma_SEMAR       SemMar_SEMAR          pf_1              pf_2        
##  Min.   :    0.00   Min.   : 0.0000   Min.   :  0.000   Min.   :  0.000  
##  1st Qu.:    0.00   1st Qu.: 0.0000   1st Qu.:  0.000   1st Qu.:  0.000  
##  Median :    0.00   Median : 0.0000   Median :  0.000   Median :  0.000  
##  Mean   :   61.44   Mean   : 0.2225   Mean   :  1.651   Mean   :  1.853  
##  3rd Qu.:    0.00   3rd Qu.: 0.0000   3rd Qu.:  0.000   3rd Qu.:  0.000  
##  Max.   :55615.00   Max.   :65.0000   Max.   :684.000   Max.   :773.000  
##                                                                          
##       pf_4                pf_5             pf_6              pf_7        
##  Min.   :    0.000   Min.   :   0.0   Min.   :  0.000   Min.   : 0.0000  
##  1st Qu.:    0.000   1st Qu.:   0.0   1st Qu.:  0.000   1st Qu.: 0.0000  
##  Median :    0.000   Median :   0.0   Median :  0.000   Median : 0.0000  
##  Mean   :   39.361   Mean   :  13.1   Mean   :  1.747   Mean   : 0.2882  
##  3rd Qu.:    0.159   3rd Qu.:   0.0   3rd Qu.:  0.000   3rd Qu.: 0.0000  
##  Max.   :23353.372   Max.   :1271.4   Max.   :129.176   Max.   :85.2828  
##                                                                          
##       pf_8              pf_9             pf_10             pf_11        
##  Min.   :    0.0   Min.   :   0.00   Min.   :   0.00   Min.   : 0.0000  
##  1st Qu.:    0.0   1st Qu.:   0.00   1st Qu.:   0.00   1st Qu.: 0.0000  
##  Median :    0.0   Median :   0.00   Median :   0.00   Median : 0.0000  
##  Mean   : 1110.5   Mean   :  17.06   Mean   :  21.62   Mean   : 0.4148  
##  3rd Qu.:  112.3   3rd Qu.:   0.00   3rd Qu.:   0.00   3rd Qu.: 0.0000  
##  Max.   :61605.7   Max.   :3212.79   Max.   :7873.58   Max.   :89.0000  
##                                                                         
##       gn_1               gn_2               gn_4               gn_6        
##  Min.   :0.000000   Min.   : 0.00000   Min.   :   0.000   Min.   : 0.0000  
##  1st Qu.:0.000000   1st Qu.: 0.00000   1st Qu.:   0.000   1st Qu.: 0.0000  
##  Median :0.000000   Median : 0.00000   Median :   0.000   Median : 0.0000  
##  Mean   :0.003788   Mean   : 0.02462   Mean   :   4.529   Mean   : 0.2393  
##  3rd Qu.:0.000000   3rd Qu.: 0.00000   3rd Qu.:   0.000   3rd Qu.: 0.0000  
##  Max.   :3.000000   Max.   :15.00000   Max.   :1408.128   Max.   :43.8430  
##                                                                            
##       gn_8              gn_9             gn_10              gn_11         
##  Min.   :   0.00   Min.   :   0.00   Min.   :   0.000   Min.   :0.000000  
##  1st Qu.:   0.00   1st Qu.:   0.00   1st Qu.:   0.000   1st Qu.:0.000000  
##  Median :   0.00   Median :   0.00   Median :   0.000   Median :0.000000  
##  Mean   :  69.42   Mean   :  12.26   Mean   :   2.991   Mean   :0.001894  
##  3rd Qu.:   0.00   3rd Qu.:   0.00   3rd Qu.:   0.000   3rd Qu.:0.000000  
##  Max.   :8648.12   Max.   :2005.21   Max.   :2598.805   Max.   :2.000000  
##                                                                           
##      gn_12             gn_13             pob_men           pob_wom       
##  Min.   : 0.0000   Min.   : 0.00000   Min.   : 222978   Min.   : 211340  
##  1st Qu.: 0.0000   1st Qu.: 0.00000   1st Qu.: 730338   1st Qu.: 753227  
##  Median : 0.0000   Median : 0.00000   Median :1296480   Median :1317132  
##  Mean   : 0.2735   Mean   : 0.09776   Mean   :1685349   Mean   :1752914  
##  3rd Qu.: 0.0000   3rd Qu.: 0.00000   3rd Qu.:1980471   3rd Qu.:2101332  
##  Max.   :82.4310   Max.   :28.67000   Max.   :8683082   Max.   :9089378  
##                                                                          
##       pob               homicr            hmenr            hwomenr       
##  Min.   :  434318   Min.   :  0.000   Min.   :  0.000   Min.   : 0.0000  
##  1st Qu.: 1482097   1st Qu.:  2.066   1st Qu.:  3.318   1st Qu.: 0.4069  
##  Median : 2613822   Median :  8.232   Median : 14.186   Median : 2.1389  
##  Mean   : 3438263   Mean   : 14.292   Mean   : 25.697   Mean   : 3.0404  
##  3rd Qu.: 4072705   3rd Qu.: 17.119   3rd Qu.: 31.067   3rd Qu.: 3.6877  
##  Max.   :17772460   Max.   :197.005   Max.   :347.275   Max.   :47.7861  
##                                                                          
##    hgunswomr         hgunsmenr          homiguns_r       heroin_average 
##  Min.   : 0.0000   Min.   :  0.0000   Min.   :  0.0000   Min.   :230.0  
##  1st Qu.: 0.0000   1st Qu.:  0.9412   1st Qu.:  0.5076   1st Qu.:265.0  
##  Median : 0.6018   Median :  6.9671   Median :  3.7233   Median :307.0  
##  Mean   : 1.4797   Mean   : 17.3331   Mean   :  9.3127   Mean   :320.3  
##  3rd Qu.: 1.5435   3rd Qu.: 20.3472   3rd Qu.: 10.7959   3rd Qu.:374.0  
##  Max.   :38.4259   Max.   :296.6719   Max.   :167.0961   Max.   :481.0  
##                                                          NA's   :64     
##  heroin_average_inflation_justed2 heroin_average_adjusted_purity
##  Min.   :286.0                    Min.   : 634.0                
##  1st Qu.:331.0                    1st Qu.: 768.0                
##  Median :394.0                    Median : 867.0                
##  Mean   :449.9                    Mean   : 910.4                
##  3rd Qu.:560.0                    3rd Qu.: 983.0                
##  Max.   :886.0                    Max.   :1561.0                
##  NA's   :64                       NA's   :64                    
##  heorin_average_adjusted_purity_i heroin_average_wholesale
##  Min.   : 788                     Min.   : 50750          
##  1st Qu.: 949                     1st Qu.: 57500          
##  Median :1077                     Median : 65500          
##  Mean   :1287                     Mean   : 86825          
##  3rd Qu.:1404                     3rd Qu.:129375          
##  Max.   :3091                     Max.   :162500          
##  NA's   :64                       NA's   :64              
##  heroin_average_inflation_adjuste cocaine_average_wholesale
##  Min.   : 53000                   Min.   :20500            
##  1st Qu.: 67770                   1st Qu.:26500            
##  Median : 86138                   Median :29000            
##  Mean   :129889                   Mean   :30711            
##  3rd Qu.:208621                   3rd Qu.:32550            
##  Max.   :321782                   Max.   :48300            
##  NA's   :64                       NA's   :32               
##  cocaine_average_wholesale_inflat cocaine_average 
##  Min.   :26798                    Min.   : 64.00  
##  1st Qu.:30848                    1st Qu.: 72.00  
##  Median :33612                    Median : 77.00  
##  Mean   :43667                    Mean   : 83.75  
##  3rd Qu.:51537                    3rd Qu.: 96.50  
##  Max.   :95643                    Max.   :120.00  
##  NA's   :64                       NA's   :32      
##  cocaine_average_inflation_adjust cocaine_average_adjusted_purity
##  Min.   : 82.0                    Min.   : 93.0                  
##  1st Qu.: 99.0                    1st Qu.:108.0                  
##  Median :109.0                    Median :127.0                  
##  Mean   :110.8                    Mean   :141.9                  
##  3rd Qu.:120.0                    3rd Qu.:178.0                  
##  Max.   :158.0                    Max.   :221.0                  
##  NA's   :64                       NA's   :64                     
##  cocaine_average_adjusted_purity_     _merge          year          
##  Min.   :118.0                    Min.   :1.000   Length:1056       
##  1st Qu.:165.0                    1st Qu.:3.000   Class :character  
##  Median :181.0                    Median :3.000   Mode  :character  
##  Mean   :185.5                    Mean   :2.939                     
##  3rd Qu.:213.0                    3rd Qu.:3.000                     
##  Max.   :274.0                    Max.   :3.000                     
##  NA's   :64                                                         
##    incocaina           inheroin       carte_trasiegoC    carte_trasiegoH   
##  Min.   :    0.00   Min.   :  0.000   Length:1056        Length:1056       
##  1st Qu.:    0.00   1st Qu.:  0.000   Class :character   Class :character  
##  Median :    0.09   Median :  0.000   Mode  :character   Mode  :character  
##  Mean   :  240.95   Mean   :  5.015                                        
##  3rd Qu.:   26.53   3rd Qu.:  0.000                                        
##  Max.   :46718.38   Max.   :340.560                                        
## 
dim(base)
## [1] 1056   97

Se construyó una base de 97 variables.

newggslopegraph(base, year, hwomenr, nom_ent,
                Title = "Evolución de tasa de homicidios por cada cien mil mujeres",
                SubTitle = "Entidad Federativas, 1990-2022",
                Caption = "R CHARTS",
                XTextSize = 7,    # Tamaño textos eje X
                YTextSize = 3,     # Tamaño grupos
                TitleTextSize = 7,    # Tamaño título
                SubTitleTextSize = 6, # Tamaño subtítulo
                CaptionTextSize = 7,  # Tamaño caption
                TitleJustify = "right",    # Justificado título
                SubTitleJustify = "right", # Justificado subtítulo
                CaptionJustify = "left",   # Justificado caption
                DataTextSize = 1) # Tamaño de los valores
## Warning: ggrepel: 32 unlabeled data points (too many overlaps). Consider
## increasing max.overlaps
## Warning: ggrepel: 12 unlabeled data points (too many overlaps). Consider
## increasing max.overlaps

newggslopegraph(base, year, hgunswomr, nom_ent,
                Title = "Evolución de tasa de homicidios por cada cien mil mujeres con armas de fuego",
                SubTitle = "Entidad Federativas, 1990-2022",
                Caption = "R CHARTS",
                XTextSize = 7,    # Tamaño textos eje X
                YTextSize = 3,     # Tamaño grupos
                TitleTextSize = 7,    # Tamaño título
                SubTitleTextSize = 6, # Tamaño subtítulo
                CaptionTextSize = 7,  # Tamaño caption
                TitleJustify = "right",    # Justificado título
                SubTitleJustify = "right", # Justificado subtítulo
                CaptionJustify = "left",   # Justificado caption
                DataTextSize = 1) # Tamaño de los valores
## Warning: ggrepel: 32 unlabeled data points (too many overlaps). Consider
## increasing max.overlaps
## Warning: ggrepel: 14 unlabeled data points (too many overlaps). Consider
## increasing max.overlaps

newggslopegraph(base, year, hmenr, nom_ent,
                Title = "Evolución de tasa de homicidios por cada cien mil hombres",
                SubTitle = "Entidad Federativas, 1990-2022",
                Caption = "R CHARTS",
                XTextSize = 7,    # Tamaño textos eje X
                YTextSize = 3,     # Tamaño grupos
                TitleTextSize = 7,    # Tamaño título
                SubTitleTextSize = 6, # Tamaño subtítulo
                CaptionTextSize = 7,  # Tamaño caption
                TitleJustify = "right",    # Justificado título
                SubTitleJustify = "right", # Justificado subtítulo
                CaptionJustify = "left",   # Justificado caption
                DataTextSize = 1) # Tamaño de los valores
## Warning: ggrepel: 32 unlabeled data points (too many overlaps). Consider
## increasing max.overlaps
## Warning: ggrepel: 8 unlabeled data points (too many overlaps). Consider
## increasing max.overlaps

newggslopegraph(base, year, hgunsmenr, nom_ent,
                Title = "Evolución de tasa de homicidios por cada cien mil hombres con armas de fuego",
                SubTitle = "Entidad Federativas, 1990-2022",
                Caption = "R CHARTS",
                XTextSize = 7,    # Tamaño textos eje X
                YTextSize = 3,     # Tamaño grupos
                TitleTextSize = 7,    # Tamaño título
                SubTitleTextSize = 6, # Tamaño subtítulo
                CaptionTextSize = 7,  # Tamaño caption
                TitleJustify = "right",    # Justificado título
                SubTitleJustify = "right", # Justificado subtítulo
                CaptionJustify = "left",   # Justificado caption
                DataTextSize = 1) # Tamaño de los valores
## Warning: ggrepel: 32 unlabeled data points (too many overlaps). Consider
## increasing max.overlaps
## Warning: ggrepel: 12 unlabeled data points (too many overlaps). Consider
## increasing max.overlaps

Precios de drogas

ggplot(base, aes(x =anio_ocur, y =heroin_average_wholesale)) +
  geom_area(alpha = 0.5)+
labs(title = "Precios de heroina por mayoreo en Estados Unidos de América",
       subtitle = "1990-2021",
       caption = "Los datos pueden ser consultados en https://dataunodc.un.org/dp-drug-prices",
       tag = "Fig. 4")+
theme(plot.caption.position = "plot",
        plot.caption = element_text(hjust = 0)) 
## Warning: Removed 64 rows containing non-finite values (`stat_align()`).

ggplot(base, aes(x =anio_ocur, y =cocaine_average_wholesale)) +
  geom_area(alpha = 0.5)+
labs(title = "Precios de Cocaina por mayoreo en Estados Unidos de América",
       subtitle = "1990-2021",
       caption = "Los datos pueden ser consultados en https://dataunodc.un.org/dp-drug-prices",
       tag = "Fig. 4")+
theme(plot.caption.position = "plot",
        plot.caption = element_text(hjust = 0)) 
## Warning: Removed 32 rows containing non-finite values (`stat_align()`).

Incautaciones en México

ggplot(base, aes(x = anio_ocur, y =incocaina , color = cve_ent)) +
  geom_area(show.legend = FALSE) +
  facet_wrap(~nom_ent , scales = "free") +
  theme(strip.text = element_text(size = 6),
        strip.background = element_blank()) +
  labs(title = "Kilogramos de cocaina incautados por entidad federativa",
       subtitle = "1990-2022",
       caption = "",
       tag = "Fig. 8")+
theme(plot.caption.position = "plot",
        plot.caption = element_text(hjust = 0)) 

ggplot(base, aes(x = anio_ocur, y =inheroin , color = cve_ent)) +
  geom_area(show.legend = FALSE) +
  facet_wrap(~nom_ent , scales = "free") +
  theme(strip.text = element_text(size = 6),
        strip.background = element_blank()) +
  labs(title = "Kilogramos de heroina incautados por entidad federativa",
       subtitle = "1990-2022",
       caption = "",
       tag = "Fig. 8")+
theme(plot.caption.position = "plot",
        plot.caption = element_text(hjust = 0)) 

##Método

Para efectos de esta investigación se realizo un modelo de panel de datos con efectos fijos y aleatorio (Sin logaritmos)

modelo_pool <- plm(homiguns_r ~ carte_trasiegoH*heroin_average_wholesale, data=base, model="pooling")
summary(modelo_pool)
## Pooling Model
## 
## Call:
## plm(formula = homiguns_r ~ carte_trasiegoH * heroin_average_wholesale, 
##     data = base, model = "pooling")
## 
## Balanced Panel: n = 31, T = 32, N = 992
## 
## Residuals:
##     Min.  1st Qu.   Median  3rd Qu.     Max. 
## -20.2302  -5.3758  -1.0687   1.0800 136.8617 
## 
## Coefficients:
##                                              Estimate  Std. Error t-value
## (Intercept)                                1.3366e+01  1.0648e+00 12.5525
## carte_trasiegoH1                           2.0320e+01  3.2383e+00  6.2751
## heroin_average_wholesale                  -8.8898e-05  1.0448e-05 -8.5085
## carte_trasiegoH1:heroin_average_wholesale -1.4490e-04  4.8215e-05 -3.0053
##                                            Pr(>|t|)    
## (Intercept)                               < 2.2e-16 ***
## carte_trasiegoH1                          5.221e-10 ***
## heroin_average_wholesale                  < 2.2e-16 ***
## carte_trasiegoH1:heroin_average_wholesale   0.00272 ** 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Total Sum of Squares:    181410
## Residual Sum of Squares: 134860
## R-Squared:      0.2566
## Adj. R-Squared: 0.25434
## F-statistic: 113.675 on 3 and 988 DF, p-value: < 2.22e-16
modelo_fe <- plm(homiguns_r ~ carte_trasiegoH*heroin_average_wholesale, data=base, model="within")
summary(modelo_fe)
## Oneway (individual) effect Within Model
## 
## Call:
## plm(formula = homiguns_r ~ carte_trasiegoH * heroin_average_wholesale, 
##     data = base, model = "within")
## 
## Balanced Panel: n = 31, T = 32, N = 992
## 
## Residuals:
##       Min.    1st Qu.     Median    3rd Qu.       Max. 
## -24.076324  -3.885631  -0.041384   0.570211 129.784309 
## 
## Coefficients:
##                                              Estimate  Std. Error t-value
## carte_trasiegoH1                           1.4222e+01  3.2851e+00  4.3291
## carte_trasiegoH1:heroin_average_wholesale -7.2230e-05  4.8344e-05 -1.4941
##                                            Pr(>|t|)    
## carte_trasiegoH1                          1.654e-05 ***
## carte_trasiegoH1:heroin_average_wholesale    0.1355    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Total Sum of Squares:    132220
## Residual Sum of Squares: 119610
## R-Squared:      0.095356
## Adj. R-Squared: 0.06517
## F-statistic: 50.5428 on 2 and 959 DF, p-value: < 2.22e-16
pFtest(modelo_fe, modelo_pool)
## 
##  F test for individual effects
## 
## data:  homiguns_r ~ carte_trasiegoH * heroin_average_wholesale
## F = 4.2179, df1 = 29, df2 = 959, p-value = 1.757e-12
## alternative hypothesis: significant effects

Para un modelo con efectos aleatorios

wallace<- plm(homicr ~ carte_trasiegoH*heroin_average_wholesale, data=base, model="random", random.method = "walhus" )
summary(wallace)
## Oneway (individual) effect Random Effect Model 
##    (Wallace-Hussain's transformation)
## 
## Call:
## plm(formula = homicr ~ carte_trasiegoH * heroin_average_wholesale, 
##     data = base, model = "random", random.method = "walhus")
## 
## Balanced Panel: n = 31, T = 32, N = 992
## 
## Effects:
##                   var std.dev share
## idiosyncratic 193.756  13.920 0.907
## individual     19.874   4.458 0.093
## theta: 0.5168
## 
## Residuals:
##      Min.   1st Qu.    Median   3rd Qu.      Max. 
## -29.44837  -6.05228  -0.70123   0.80287 155.43359 
## 
## Coefficients:
##                                              Estimate  Std. Error z-value
## (Intercept)                                2.3074e+01  2.3574e+00  9.7879
## carte_trasiegoH1                           2.0942e+01  4.0383e+00  5.1857
## heroin_average_wholesale                  -1.5239e-04  2.4288e-05 -6.2744
## carte_trasiegoH1:heroin_average_wholesale -1.2996e-04  5.9604e-05 -2.1803
##                                            Pr(>|z|)    
## (Intercept)                               < 2.2e-16 ***
## carte_trasiegoH1                          2.152e-07 ***
## heroin_average_wholesale                  3.509e-10 ***
## carte_trasiegoH1:heroin_average_wholesale   0.02924 *  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Total Sum of Squares:    228820
## Residual Sum of Squares: 191760
## R-Squared:      0.16198
## Adj. R-Squared: 0.15943
## Chisq: 190.967 on 3 DF, p-value: < 2.22e-16
ame<- plm(homicr ~ carte_trasiegoH*heroin_average_wholesale, data=base, model="random", random.method = "amemiya" )
summary(ame)
## Oneway (individual) effect Random Effect Model 
##    (Amemiya's transformation)
## 
## Call:
## plm(formula = homicr ~ carte_trasiegoH * heroin_average_wholesale, 
##     data = base, model = "random", random.method = "amemiya")
## 
## Balanced Panel: n = 31, T = 32, N = 992
## 
## Effects:
##                   var std.dev share
## idiosyncratic 192.930  13.890 0.771
## individual     57.390   7.576 0.229
## theta: 0.6917
## 
## Residuals:
##      Min.   1st Qu.    Median   3rd Qu.      Max. 
## -30.18818  -5.35439  -0.45234   0.66731 153.97228 
## 
## Coefficients:
##                                              Estimate  Std. Error z-value
## (Intercept)                                2.3278e+01  3.5667e+00  6.5265
## carte_trasiegoH1                           1.9855e+01  4.0333e+00  4.9228
## heroin_average_wholesale                  -1.5398e-04  3.7198e-05 -4.1396
## carte_trasiegoH1:heroin_average_wholesale -1.1739e-04  5.9427e-05 -1.9754
##                                            Pr(>|z|)    
## (Intercept)                               6.731e-11 ***
## carte_trasiegoH1                          8.533e-07 ***
## heroin_average_wholesale                  3.479e-05 ***
## carte_trasiegoH1:heroin_average_wholesale   0.04822 *  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Total Sum of Squares:    215110
## Residual Sum of Squares: 188000
## R-Squared:      0.12602
## Adj. R-Squared: 0.12337
## Chisq: 142.459 on 3 DF, p-value: < 2.22e-16
ner<- plm(homicr ~ carte_trasiegoH*heroin_average_wholesale, data=base, model="random", random.method = "nerlove" )
summary(wallace)
## Oneway (individual) effect Random Effect Model 
##    (Wallace-Hussain's transformation)
## 
## Call:
## plm(formula = homicr ~ carte_trasiegoH * heroin_average_wholesale, 
##     data = base, model = "random", random.method = "walhus")
## 
## Balanced Panel: n = 31, T = 32, N = 992
## 
## Effects:
##                   var std.dev share
## idiosyncratic 193.756  13.920 0.907
## individual     19.874   4.458 0.093
## theta: 0.5168
## 
## Residuals:
##      Min.   1st Qu.    Median   3rd Qu.      Max. 
## -29.44837  -6.05228  -0.70123   0.80287 155.43359 
## 
## Coefficients:
##                                              Estimate  Std. Error z-value
## (Intercept)                                2.3074e+01  2.3574e+00  9.7879
## carte_trasiegoH1                           2.0942e+01  4.0383e+00  5.1857
## heroin_average_wholesale                  -1.5239e-04  2.4288e-05 -6.2744
## carte_trasiegoH1:heroin_average_wholesale -1.2996e-04  5.9604e-05 -2.1803
##                                            Pr(>|z|)    
## (Intercept)                               < 2.2e-16 ***
## carte_trasiegoH1                          2.152e-07 ***
## heroin_average_wholesale                  3.509e-10 ***
## carte_trasiegoH1:heroin_average_wholesale   0.02924 *  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Total Sum of Squares:    228820
## Residual Sum of Squares: 191760
## R-Squared:      0.16198
## Adj. R-Squared: 0.15943
## Chisq: 190.967 on 3 DF, p-value: < 2.22e-16
phtest(wallace, modelo_fe)
## 
##  Hausman Test
## 
## data:  homicr ~ carte_trasiegoH * heroin_average_wholesale
## chisq = 21.824, df = 2, p-value = 1.824e-05
## alternative hypothesis: one model is inconsistent
phtest(ner, modelo_fe)
## 
##  Hausman Test
## 
## data:  homicr ~ carte_trasiegoH * heroin_average_wholesale
## chisq = 17.02, df = 2, p-value = 0.0002015
## alternative hypothesis: one model is inconsistent
phtest(ame, modelo_fe)
## 
##  Hausman Test
## 
## data:  homicr ~ carte_trasiegoH * heroin_average_wholesale
## chisq = 17.417, df = 2, p-value = 0.0001652
## alternative hypothesis: one model is inconsistent

##COMENTARIOS FINALES

-Los niveles de violencia han incrementado durante 1997.

-La tasa de homicidios máxima es 197 homicidios por cada cien mil habitantes. La media responde14 homicidios.

Datos diferenciados:

-La tasa de homicidios de los hombres durante 1990-2022 oscila en 347 homicidios por cada cien mil habitantes. La media es 25 homicidios.

-Para el caso diferenciado de las mujeres, tasa máxima es 47 homicidios por cada cien mi mujeres habitantes. Mientras su media es 3 homicidios.

-Precio de droga varia durante el tiempo. Anteriormente, el precio era mayor.

-El valor máximo de la heroína 162,500 dls sin ajustarse a la inflación

-Existe débil correlación minima entre homicidios y la presencia de drogas. -Se realizó una serie de modelos con efectos fijos y aleatorios para conocer la relación entre variables; sin embargo, no se ahondo en el análisis de coeficientes.

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